Overview

Dataset statistics

Number of variables22
Number of observations929
Missing cells9094
Missing cells (%)44.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory221.5 KiB
Average record size in memory244.1 B

Variable types

Numeric10
Unsupported7
Categorical5

Warnings

danger has constant value "1.0" Constant
operation_car has constant value "16.0" Constant
sender has constant value "0.0" Constant
operation_date has a high cardinality: 524 distinct values High cardinality
car_number is highly correlated with rodvagHigh correlation
rodvag is highly correlated with car_numberHigh correlation
operation_car is highly correlated with danger and 2 other fieldsHigh correlation
danger is highly correlated with operation_car and 2 other fieldsHigh correlation
sender is highly correlated with operation_car and 2 other fieldsHigh correlation
adm is highly correlated with operation_car and 2 other fieldsHigh correlation
index_train has 929 (100.0%) missing values Missing
destination_esr has 417 (44.9%) missing values Missing
danger has 923 (99.4%) missing values Missing
gruz has 417 (44.9%) missing values Missing
loaded has 929 (100.0%) missing values Missing
operation_train has 929 (100.0%) missing values Missing
receiver has 417 (44.9%) missing values Missing
rod_train has 929 (100.0%) missing values Missing
sender has 417 (44.9%) missing values Missing
ssp_station_esr has 929 (100.0%) missing values Missing
ssp_station_id has 929 (100.0%) missing values Missing
weight_brutto has 929 (100.0%) missing values Missing
df_index has unique values Unique
index_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
loaded is an unsupported type, check if it needs cleaning or further analysis Unsupported
operation_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
rod_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
ssp_station_esr is an unsupported type, check if it needs cleaning or further analysis Unsupported
ssp_station_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
weight_brutto is an unsupported type, check if it needs cleaning or further analysis Unsupported
receiver has 25 (2.7%) zeros Zeros

Reproduction

Analysis started2021-04-16 09:39:00.438244
Analysis finished2021-04-16 09:39:18.883727
Duration18.45 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct929
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2362461.623
Minimum4470
Maximum4184583
Zeros0
Zeros (%)0.0%
Memory size7.4 KiB
2021-04-16T15:39:19.033691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum4470
5-th percentile185111.2
Q11207323
median2513618
Q33378128
95-th percentile3987982.6
Maximum4184583
Range4180113
Interquartile range (IQR)2170805

Descriptive statistics

Standard deviation1219738.002
Coefficient of variation (CV)0.5162996048
Kurtosis-1.039013292
Mean2362461.623
Median Absolute Deviation (MAD)919358
Skewness-0.4373204778
Sum2194726848
Variance1.487760795 × 1012
MonotocityStrictly increasing
2021-04-16T15:39:19.198691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4505621
 
0.1%
14615881
 
0.1%
37000241
 
0.1%
36652101
 
0.1%
31450191
 
0.1%
36570241
 
0.1%
36590731
 
0.1%
8062121
 
0.1%
29832371
 
0.1%
15680721
 
0.1%
Other values (919)919
98.9%
ValueCountFrequency (%)
44701
0.1%
71151
0.1%
744541
0.1%
752221
0.1%
758081
0.1%
760361
0.1%
870661
0.1%
873031
0.1%
882201
0.1%
883011
0.1%
ValueCountFrequency (%)
41845831
0.1%
41767321
0.1%
41765371
0.1%
41763991
0.1%
41754261
0.1%
41721541
0.1%
41712041
0.1%
41677381
0.1%
41669491
0.1%
41659171
0.1%

index_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing929
Missing (%)100.0%
Memory size7.4 KiB

length
Real number (ℝ≥0)

Distinct8
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.053466093
Minimum1
Maximum1.36
Zeros0
Zeros (%)0.0%
Memory size7.4 KiB
2021-04-16T15:39:19.330692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31.06
95-th percentile1.36
Maximum1.36
Range0.36
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.1130419856
Coefficient of variation (CV)0.1073048163
Kurtosis2.733429993
Mean1.053466093
Median Absolute Deviation (MAD)0
Skewness2.095660333
Sum978.67
Variance0.0127784905
MonotocityNot monotonic
2021-04-16T15:39:19.444696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1654
70.4%
1.06131
 
14.1%
1.3683
 
8.9%
1.3220
 
2.2%
1.0117
 
1.8%
1.2216
 
1.7%
1.276
 
0.6%
1.112
 
0.2%
ValueCountFrequency (%)
1654
70.4%
1.0117
 
1.8%
1.06131
 
14.1%
1.112
 
0.2%
1.2216
 
1.7%
1.276
 
0.6%
1.3220
 
2.2%
1.3683
 
8.9%
ValueCountFrequency (%)
1.3683
 
8.9%
1.3220
 
2.2%
1.276
 
0.6%
1.2216
 
1.7%
1.112
 
0.2%
1.06131
 
14.1%
1.0117
 
1.8%
1654
70.4%

car_number
Real number (ℝ≥0)

HIGH CORRELATION

Distinct917
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55986585.55
Minimum24603474
Maximum96736632
Zeros0
Zeros (%)0.0%
Memory size7.4 KiB
2021-04-16T15:39:19.602696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum24603474
5-th percentile29262053.6
Q152685716
median60211778
Q362407580
95-th percentile68061130.8
Maximum96736632
Range72133158
Interquartile range (IQR)9721864

Descriptive statistics

Standard deviation13443258.08
Coefficient of variation (CV)0.2401156982
Kurtosis1.616075088
Mean55986585.55
Median Absolute Deviation (MAD)3910781
Skewness-0.05559361182
Sum5.201153798 × 1010
Variance1.807211877 × 1014
MonotocityNot monotonic
2021-04-16T15:39:19.776697image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
437385092
 
0.2%
447798662
 
0.2%
434644292
 
0.2%
426334532
 
0.2%
425061882
 
0.2%
601947842
 
0.2%
426609102
 
0.2%
431158562
 
0.2%
600998432
 
0.2%
534183802
 
0.2%
Other values (907)909
97.8%
ValueCountFrequency (%)
246034741
0.1%
246168721
0.1%
246223181
0.1%
267265961
0.1%
280313421
0.1%
280323571
0.1%
280331241
0.1%
280351291
0.1%
288183421
0.1%
290044961
0.1%
ValueCountFrequency (%)
967366321
0.1%
967365411
0.1%
967362101
0.1%
967359561
0.1%
966570931
0.1%
966555921
0.1%
966534981
0.1%
966250901
0.1%
966195981
0.1%
954185881
0.1%

destination_esr
Real number (ℝ≥0)

MISSING

Distinct82
Distinct (%)16.0%
Missing417
Missing (%)44.9%
Infinite0
Infinite (%)0.0%
Mean809242.793
Minimum27802
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size7.4 KiB
2021-04-16T15:39:19.968691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum27802
5-th percentile438603
Q1797002
median852708
Q3932601
95-th percentile946801
Maximum998100
Range970298
Interquartile range (IQR)135599

Descriptive statistics

Standard deviation161289.0809
Coefficient of variation (CV)0.1993086405
Kurtosis6.174693212
Mean809242.793
Median Absolute Deviation (MAD)70905
Skewness-2.319456137
Sum414332310
Variance2.601416761 × 1010
MonotocityNot monotonic
2021-04-16T15:39:20.155691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85270868
 
7.3%
93290250
 
5.4%
94680134
 
3.7%
79800533
 
3.6%
93260132
 
3.4%
79910126
 
2.8%
64820221
 
2.3%
80670814
 
1.5%
81420811
 
1.2%
92000211
 
1.2%
Other values (72)212
22.8%
(Missing)417
44.9%
ValueCountFrequency (%)
278021
 
0.1%
330041
 
0.1%
1520063
 
0.3%
1550041
 
0.1%
1835021
 
0.1%
1942061
 
0.1%
2054001
 
0.1%
2554091
 
0.1%
2883081
 
0.1%
29170710
1.1%
ValueCountFrequency (%)
9981003
 
0.3%
9845024
 
0.4%
9507181
 
0.1%
94680134
3.7%
9421052
 
0.2%
9400069
 
1.0%
9376051
 
0.1%
93290250
5.4%
93260132
3.4%
9301083
 
0.3%

adm
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size55.5 KiB
20.0
924 
26.0
 
3
27.0
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3716
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20.0
2nd row20.0
3rd row20.0
4th row20.0
5th row20.0
ValueCountFrequency (%)
20.0924
99.5%
26.03
 
0.3%
27.02
 
0.2%
2021-04-16T15:39:20.451692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T15:39:20.535691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
20.0924
99.5%
26.03
 
0.3%
27.02
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01853
49.9%
2929
25.0%
.929
25.0%
63
 
0.1%
72
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2787
75.0%
Other Punctuation929
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
01853
66.5%
2929
33.3%
63
 
0.1%
72
 
0.1%
ValueCountFrequency (%)
.929
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3716
100.0%

Most frequent character per script

ValueCountFrequency (%)
01853
49.9%
2929
25.0%
.929
25.0%
63
 
0.1%
72
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3716
100.0%

Most frequent character per block

ValueCountFrequency (%)
01853
49.9%
2929
25.0%
.929
25.0%
63
 
0.1%
72
 
0.1%

danger
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)16.7%
Missing923
Missing (%)99.4%
Memory size36.5 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.06
 
0.6%
(Missing)923
99.4%
2021-04-16T15:39:20.752729image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T15:39:20.833731image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.06
100.0%

Most occurring characters

ValueCountFrequency (%)
16
33.3%
.6
33.3%
06
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12
66.7%
Other Punctuation6
33.3%

Most frequent character per category

ValueCountFrequency (%)
16
50.0%
06
50.0%
ValueCountFrequency (%)
.6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common18
100.0%

Most frequent character per script

ValueCountFrequency (%)
16
33.3%
.6
33.3%
06
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII18
100.0%

Most frequent character per block

ValueCountFrequency (%)
16
33.3%
.6
33.3%
06
33.3%

gruz
Real number (ℝ≥0)

MISSING

Distinct49
Distinct (%)9.6%
Missing417
Missing (%)44.9%
Infinite0
Infinite (%)0.0%
Mean350966.6426
Minimum141105
Maximum731062
Zeros0
Zeros (%)0.0%
Memory size7.4 KiB
2021-04-16T15:39:20.932691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum141105
5-th percentile241262.55
Q1323058
median324169
Q3351306
95-th percentile485572
Maximum731062
Range589957
Interquartile range (IQR)28248

Descriptive statistics

Standard deviation90565.89377
Coefficient of variation (CV)0.258047013
Kurtosis5.08170106
Mean350966.6426
Median Absolute Deviation (MAD)27137
Skewness1.559379617
Sum179694921
Variance8202181115
MonotocityNot monotonic
2021-04-16T15:39:21.111730image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
351306147
 
15.8%
324169136
 
14.6%
32302437
 
4.0%
47306220
 
2.2%
14110514
 
1.5%
30328513
 
1.4%
26422012
 
1.3%
30203212
 
1.3%
69322710
 
1.1%
3230589
 
1.0%
Other values (39)102
 
11.0%
(Missing)417
44.9%
ValueCountFrequency (%)
14110514
1.5%
1510374
 
0.4%
2321645
 
0.5%
2412293
 
0.3%
2412901
 
0.1%
2413182
 
0.2%
2413563
 
0.3%
2640422
 
0.2%
26422012
1.3%
3010784
 
0.4%
ValueCountFrequency (%)
7310621
 
0.1%
69322710
1.1%
6610481
 
0.1%
6341591
 
0.1%
6341103
 
0.3%
6340893
 
0.3%
6322421
 
0.1%
6320981
 
0.1%
5420841
 
0.1%
5410241
 
0.1%

loaded
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing929
Missing (%)100.0%
Memory size7.4 KiB

operation_car
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.5 KiB
16.0
929 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3716
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16.0
2nd row16.0
3rd row16.0
4th row16.0
5th row16.0
ValueCountFrequency (%)
16.0929
100.0%
2021-04-16T15:39:21.377696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T15:39:21.450692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
16.0929
100.0%

Most occurring characters

ValueCountFrequency (%)
1929
25.0%
6929
25.0%
.929
25.0%
0929
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2787
75.0%
Other Punctuation929
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
1929
33.3%
6929
33.3%
0929
33.3%
ValueCountFrequency (%)
.929
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3716
100.0%

Most frequent character per script

ValueCountFrequency (%)
1929
25.0%
6929
25.0%
.929
25.0%
0929
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3716
100.0%

Most frequent character per block

ValueCountFrequency (%)
1929
25.0%
6929
25.0%
.929
25.0%
0929
25.0%

operation_date
Categorical

HIGH CARDINALITY

Distinct524
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Memory size69.1 KiB
2020-07-17 06:30:00
66 
2020-07-06 01:20:00
66 
2020-07-20 12:13:00
 
59
2020-07-29 03:45:00
 
53
2020-07-27 06:37:00
 
44
Other values (519)
641 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters17651
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique508 ?
Unique (%)54.7%

Sample

1st row2020-07-16 16:11:00
2nd row2020-07-16 11:04:00
3rd row2020-07-17 12:22:00
4th row2020-07-17 12:18:00
5th row2020-07-17 11:23:00
ValueCountFrequency (%)
2020-07-17 06:30:0066
 
7.1%
2020-07-06 01:20:0066
 
7.1%
2020-07-20 12:13:0059
 
6.4%
2020-07-29 03:45:0053
 
5.7%
2020-07-27 06:37:0044
 
4.7%
2020-07-09 05:25:0028
 
3.0%
2020-07-11 15:40:0022
 
2.4%
2020-07-14 10:20:0022
 
2.4%
2020-07-12 02:55:0017
 
1.8%
2020-07-14 02:29:0013
 
1.4%
Other values (514)539
58.0%
2021-04-16T15:39:21.789691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-07-1795
 
5.1%
2020-07-0685
 
4.6%
2020-07-2984
 
4.5%
2020-07-2082
 
4.4%
2020-07-2772
 
3.9%
06:30:0066
 
3.6%
01:20:0066
 
3.6%
12:13:0059
 
3.2%
03:45:0054
 
2.9%
2020-07-1448
 
2.6%
Other values (395)1147
61.7%

Most occurring characters

ValueCountFrequency (%)
05832
33.0%
22726
15.4%
-1858
 
10.5%
:1858
 
10.5%
71278
 
7.2%
11201
 
6.8%
929
 
5.3%
5467
 
2.6%
3447
 
2.5%
6370
 
2.1%
Other values (3)685
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number13006
73.7%
Dash Punctuation1858
 
10.5%
Other Punctuation1858
 
10.5%
Space Separator929
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
05832
44.8%
22726
21.0%
71278
 
9.8%
11201
 
9.2%
5467
 
3.6%
3447
 
3.4%
6370
 
2.8%
4270
 
2.1%
9259
 
2.0%
8156
 
1.2%
ValueCountFrequency (%)
-1858
100.0%
ValueCountFrequency (%)
929
100.0%
ValueCountFrequency (%)
:1858
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common17651
100.0%

Most frequent character per script

ValueCountFrequency (%)
05832
33.0%
22726
15.4%
-1858
 
10.5%
:1858
 
10.5%
71278
 
7.2%
11201
 
6.8%
929
 
5.3%
5467
 
2.6%
3447
 
2.5%
6370
 
2.1%
Other values (3)685
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII17651
100.0%

Most frequent character per block

ValueCountFrequency (%)
05832
33.0%
22726
15.4%
-1858
 
10.5%
:1858
 
10.5%
71278
 
7.2%
11201
 
6.8%
929
 
5.3%
5467
 
2.6%
3447
 
2.5%
6370
 
2.1%
Other values (3)685
 
3.9%

operation_st_esr
Real number (ℝ≥0)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean952865.6964
Minimum936903
Maximum989309
Zeros0
Zeros (%)0.0%
Memory size7.4 KiB
2021-04-16T15:39:22.127694image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum936903
5-th percentile946801
Q1946801
median947005
Q3947005
95-th percentile989205
Maximum989309
Range52406
Interquartile range (IQR)204

Descriptive statistics

Standard deviation14016.8233
Coefficient of variation (CV)0.01471017726
Kurtosis2.034245319
Mean952865.6964
Median Absolute Deviation (MAD)0
Skewness1.976797908
Sum885212232
Variance196471335.5
MonotocityNot monotonic
2021-04-16T15:39:22.296694image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
947005508
54.7%
946801276
29.7%
98920581
 
8.7%
97960859
 
6.4%
9893094
 
0.4%
9369031
 
0.1%
ValueCountFrequency (%)
9369031
 
0.1%
946801276
29.7%
947005508
54.7%
97960859
 
6.4%
98920581
 
8.7%
9893094
 
0.4%
ValueCountFrequency (%)
9893094
 
0.4%
98920581
 
8.7%
97960859
 
6.4%
947005508
54.7%
946801276
29.7%
9369031
 
0.1%

operation_st_id
Real number (ℝ≥0)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001251047
Minimum2000037498
Maximum2002026607
Zeros0
Zeros (%)0.0%
Memory size7.4 KiB
2021-04-16T15:39:22.414693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2000037498
5-th percentile2000037862
Q12000037862
median2002025275
Q32002025275
95-th percentile2002026607
Maximum2002026607
Range1989109
Interquartile range (IQR)1987413

Descriptive statistics

Standard deviation969656.204
Coefficient of variation (CV)0.0004845250202
Kurtosis-1.798466089
Mean2001251047
Median Absolute Deviation (MAD)0
Skewness-0.4532211753
Sum1.859162223 × 1012
Variance9.402331539 × 1011
MonotocityNot monotonic
2021-04-16T15:39:22.531698image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2002025275508
54.7%
2000037862276
29.7%
200003912681
 
8.7%
200202660759
 
6.4%
20000391324
 
0.4%
20000374981
 
0.1%
ValueCountFrequency (%)
20000374981
 
0.1%
2000037862276
29.7%
200003912681
 
8.7%
20000391324
 
0.4%
2002025275508
54.7%
200202660759
 
6.4%
ValueCountFrequency (%)
200202660759
 
6.4%
2002025275508
54.7%
20000391324
 
0.4%
200003912681
 
8.7%
2000037862276
29.7%
20000374981
 
0.1%

operation_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing929
Missing (%)100.0%
Memory size7.4 KiB

receiver
Real number (ℝ≥0)

MISSING
ZEROS

Distinct91
Distinct (%)17.8%
Missing417
Missing (%)44.9%
Infinite0
Infinite (%)0.0%
Mean35348119.17
Minimum0
Maximum97966927
Zeros25
Zeros (%)2.7%
Memory size7.4 KiB
2021-04-16T15:39:22.698729image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186209.35
Q15766801
median37144780
Q356090951
95-th percentile90053767
Maximum97966927
Range97966927
Interquartile range (IQR)50324150

Descriptive statistics

Standard deviation29892462.18
Coefficient of variation (CV)0.8456591999
Kurtosis-0.8863102326
Mean35348119.17
Median Absolute Deviation (MAD)31359533
Skewness0.515853832
Sum1.809823702 × 1010
Variance8.935592951 × 1014
MonotocityNot monotonic
2021-04-16T15:39:22.870691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37144780114
 
12.3%
025
 
2.7%
576680121
 
2.3%
7762222620
 
2.2%
7473301419
 
2.0%
6886121616
 
1.7%
18714514
 
1.5%
1263150413
 
1.4%
102945412
 
1.3%
2854509512
 
1.3%
Other values (81)246
26.5%
(Missing)417
44.9%
ValueCountFrequency (%)
025
2.7%
1862001
 
0.1%
1862171
 
0.1%
1862694
 
0.4%
18642410
 
1.1%
1864473
 
0.3%
1864651
 
0.1%
1866021
 
0.1%
1866311
 
0.1%
1868491
 
0.1%
ValueCountFrequency (%)
979669271
 
0.1%
9767938110
1.1%
964172422
 
0.2%
956875838
0.9%
929640422
 
0.2%
907100992
 
0.2%
900537672
 
0.2%
897069391
 
0.1%
881595999
1.0%
881435982
 
0.2%

rodvag
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.26264801
Minimum20
Maximum95
Zeros0
Zeros (%)0.0%
Memory size7.4 KiB
2021-04-16T15:39:23.006697image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q140
median60
Q360
95-th percentile60
Maximum95
Range75
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.19612801
Coefficient of variation (CV)0.304080413
Kurtosis0.6747461584
Mean53.26264801
Median Absolute Deviation (MAD)0
Skewness-0.7512602681
Sum49481
Variance262.3145624
MonotocityNot monotonic
2021-04-16T15:39:23.120691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
60653
70.3%
20130
 
14.0%
40109
 
11.7%
9033
 
3.6%
923
 
0.3%
951
 
0.1%
ValueCountFrequency (%)
20130
 
14.0%
40109
 
11.7%
60653
70.3%
9033
 
3.6%
923
 
0.3%
951
 
0.1%
ValueCountFrequency (%)
951
 
0.1%
923
 
0.3%
9033
 
3.6%
60653
70.3%
40109
 
11.7%
20130
 
14.0%

rod_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing929
Missing (%)100.0%
Memory size7.4 KiB

sender
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)0.2%
Missing417
Missing (%)44.9%
Memory size46.4 KiB
0.0
512 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1536
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0512
55.1%
(Missing)417
44.9%
2021-04-16T15:39:23.364692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T15:39:23.440696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0512
100.0%

Most occurring characters

ValueCountFrequency (%)
01024
66.7%
.512
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1024
66.7%
Other Punctuation512
33.3%

Most frequent character per category

ValueCountFrequency (%)
01024
100.0%
ValueCountFrequency (%)
.512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1536
100.0%

Most frequent character per script

ValueCountFrequency (%)
01024
66.7%
.512
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1536
100.0%

Most frequent character per block

ValueCountFrequency (%)
01024
66.7%
.512
33.3%

ssp_station_esr
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing929
Missing (%)100.0%
Memory size7.4 KiB

ssp_station_id
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing929
Missing (%)100.0%
Memory size7.4 KiB

tare_weight
Real number (ℝ≥0)

Distinct52
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.4068891
Minimum209
Maximum272
Zeros0
Zeros (%)0.0%
Memory size7.4 KiB
2021-04-16T15:39:23.534692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum209
5-th percentile215
Q1236
median241
Q3245
95-th percentile271
Maximum272
Range63
Interquartile range (IQR)9

Descriptive statistics

Standard deviation13.70715094
Coefficient of variation (CV)0.05678028076
Kurtosis0.6753302462
Mean241.4068891
Median Absolute Deviation (MAD)4
Skewness0.3438497982
Sum224267
Variance187.885987
MonotocityNot monotonic
2021-04-16T15:39:23.698693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24090
 
9.7%
24272
 
7.8%
24366
 
7.1%
24558
 
6.2%
22551
 
5.5%
24148
 
5.2%
23846
 
5.0%
24443
 
4.6%
23942
 
4.5%
27140
 
4.3%
Other values (42)373
40.2%
ValueCountFrequency (%)
2098
0.9%
21019
2.0%
2111
 
0.1%
2122
 
0.2%
2143
 
0.3%
21519
2.0%
2172
 
0.2%
2203
 
0.3%
2211
 
0.1%
2221
 
0.1%
ValueCountFrequency (%)
27215
 
1.6%
27140
4.3%
2709
 
1.0%
2692
 
0.2%
26823
2.5%
2671
 
0.1%
26520
2.2%
2632
 
0.2%
2624
 
0.4%
2607
 
0.8%

weight_brutto
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing929
Missing (%)100.0%
Memory size7.4 KiB

Interactions

2021-04-16T15:39:01.601376image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:01.796381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:01.982377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:02.129377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:02.266376image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:02.423381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:02.601376image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:02.747418image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:02.904378image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:03.060377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:03.231376image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:03.408444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:03.560478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:03.707476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:03.884477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:04.074439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:04.222477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:04.389444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:04.749480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:04.914482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:05.089440image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:05.224477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:05.353475image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:05.517440image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:05.694478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:05.830481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:05.999476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:06.180481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:06.324441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:06.497481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:06.628477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:06.764485image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:06.938479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:07.153438image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:07.311476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:07.452444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:07.590444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:07.719440image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:07.860477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:07.993444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:08.148477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:08.280476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:08.436481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:08.569444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:08.722439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:08.881482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:09.046476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:09.359440image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:09.521478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:09.781476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:09.937442image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:10.121477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:10.293478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:10.460444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:10.632445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:10.836478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:11.041445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:11.340438image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:11.579529image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:11.810442image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:12.021438image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:12.196477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:12.406440image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:12.745440image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:12.943442image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:13.215442image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:13.447441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:13.651441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:13.838444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:14.078439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:14.248479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:14.395480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:14.536479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:14.694476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:14.865692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:15.037692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:15.189733image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:15.348693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:15.534697image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:15.715691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:15.878695image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:16.047697image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:16.343698image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:16.523691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:16.688693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:16.831692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:16.970695image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:17.132728image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:17.303692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:39:17.454728image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-16T15:39:23.887730image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-16T15:39:24.223692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-16T15:39:24.555691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-16T15:39:24.865692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-16T15:39:25.064692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-16T15:39:17.811693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-16T15:39:18.290692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-16T15:39:18.536692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-16T15:39:18.708691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
04470NaN1.0063142087781907.020.0NaN323024.0NaN16.02020-07-16 16:11:00947005.02.002025e+09NaN53384816.060.0NaN0.0NaNNaN242.0NaN
17115NaN1.0063046585924501.020.0NaN473062.0NaN16.02020-07-16 11:04:00947005.02.002025e+09NaN5785247.060.0NaN0.0NaNNaN245.0NaN
274454NaN1.3629592995923000.020.0NaN632098.0NaN16.02020-07-17 12:22:00947005.02.002025e+09NaN46696320.020.0NaN0.0NaNNaN269.0NaN
375222NaN1.3629058690923000.020.0NaN632242.0NaN16.02020-07-17 12:18:00947005.02.002025e+09NaN46696320.020.0NaN0.0NaNNaN271.0NaN
475808NaN1.3629200292946801.020.0NaN351306.0NaN16.02020-07-17 11:23:00947005.02.002025e+09NaN12631504.020.0NaN0.0NaNNaN271.0NaN
576036NaN1.3229575693850204.020.0NaN515049.0NaN16.02020-07-17 12:26:00947005.02.002025e+09NaN87383366.020.0NaN0.0NaNNaN265.0NaN
687066NaN1.0053574232NaN20.0NaNNaNNaN16.02020-07-17 06:30:00946801.02.000038e+09NaNNaN60.0NaNNaNNaNNaN226.0NaN
787303NaN1.0053574943NaN20.0NaNNaNNaN16.02020-07-17 06:30:00946801.02.000038e+09NaNNaN60.0NaNNaNNaNNaN228.0NaN
888220NaN1.0053456877NaN20.0NaNNaNNaN16.02020-07-17 06:30:00946801.02.000038e+09NaNNaN60.0NaNNaNNaNNaN238.0NaN
988301NaN1.0053476941NaN20.0NaNNaNNaN16.02020-07-17 06:30:00946801.02.000038e+09NaNNaN60.0NaNNaNNaNNaN231.0NaN

Last rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
9194165917NaN1.061282562288308.020.0NaN351306.0NaN16.02020-07-16 15:24:00947005.02.002025e+09NaN81033642.060.0NaN0.0NaNNaN244.0NaN
9204166949NaN1.061369989920002.020.0NaN473081.0NaN16.02020-07-16 10:29:00947005.02.002025e+09NaN97679381.060.0NaN0.0NaNNaN243.0NaN
9214167738NaN1.061091211798005.020.0NaN323024.0NaN16.02020-07-16 15:37:00947005.02.002025e+09NaN28545095.060.0NaN0.0NaNNaN240.0NaN
9224171204NaN1.063463988798005.020.0NaN323024.0NaN16.02020-07-16 15:43:00947005.02.002025e+09NaN28545095.060.0NaN0.0NaNNaN245.0NaN
9234172154NaN1.063525448998100.020.0NaN323024.0NaN16.02020-07-16 16:03:00947005.02.002025e+09NaN81622261.060.0NaN0.0NaNNaN238.0NaN
9244175426NaN1.063820583932601.020.0NaN324169.0NaN16.02020-07-15 19:00:00947005.02.002025e+09NaN77622226.060.0NaN0.0NaNNaN243.0NaN
9254176399NaN1.063982318798005.020.0NaN323024.0NaN16.02020-07-16 11:37:00947005.02.002025e+09NaN28545095.060.0NaN0.0NaNNaN242.0NaN
9264176537NaN1.063970883932902.020.0NaN473081.0NaN16.02020-07-16 16:45:00947005.02.002025e+09NaN14462000.060.0NaN0.0NaNNaN240.0NaN
9274176732NaN1.064037104852708.020.0NaN324169.0NaN16.02020-07-15 19:16:00947005.02.002025e+09NaN37144780.060.0NaN0.0NaNNaN240.0NaN
9284184583NaN1.064910169940006.020.0NaN324169.0NaN16.02020-07-15 19:05:00947005.02.002025e+09NaN88159599.060.0NaN0.0NaNNaN243.0NaN